diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..31e8730c0ad0404e062f843ba39a250e29107b51 --- /dev/null +++ b/README.md @@ -0,0 +1,54 @@ + +--- +license: mit +base_model: warp-ai/wuerstchen-prior +datasets: +- dongOi071102/meme-pretreatment-dataset +tags: +- wuerstchen +- text-to-image +- diffusers +- diffusers-training +- lora +inference: true +--- + +# LoRA Finetuning - dongOi071102/wuerstchen-prior-meme-lora-4 + +This pipeline was finetuned from **warp-ai/wuerstchen-prior** on the **dongOi071102/meme-pretreatment-dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a catton cat with angry face']: + +![val_imgs_grid](./val_imgs_grid.png) + + +## Pipeline usage + +You can use the pipeline like so: + +```python +from diffusers import DiffusionPipeline +import torch + +pipeline = AutoPipelineForText2Image.from_pretrained( + "warp-ai/wuerstchen", torch_dtype=float32 + ) +# load lora weights from folder: +pipeline.prior_pipe.load_lora_weights("dongOi071102/wuerstchen-prior-meme-lora-4", torch_dtype=float32) + +image = pipeline(prompt=prompt).images[0] +image.save("my_image.png") +``` + +## Training info + +These are the key hyperparameters used during training: + +* LoRA rank: 4 +* Epochs: 100 +* Learning rate: 0.0001 +* Batch size: 8 +* Gradient accumulation steps: 1 +* Image resolution: 512 +* Mixed-precision: fp16 + + +More information on all the CLI arguments and the environment are available on your [`wandb` run 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